1. Field of the Invention
The invention relates to an apparatus and method for monitoring, analyzing and utilizing brainwave data.
2. Description of the Related Art
The human brain can be viewed as functionally interrelated synaptic pathways and neural activation matrices. These pathways and matrices are predisposed to functionally respond and further develop with use. This is the basis for all higher learning. At any given moment, the brain radiates a plethora of events, tasks and states which are related to neural discharge patterns. Neural discharge patterns can be defined as brainwave signatures. A brainwave signature can be associated with human cognitive states, learning, intellectual ability and disability.
Considerable research has been directed to biological feedback of brainwave signals known as electroencephalogram (EEG) signals. One conventional neurophysiological study established a functional relationship between behavior and bandwidths in the 12-15 Hz range relating to sensorimotor cortex rhythm EEG activity (SMR). Sterman, M. B., Lopresti, R. W., & Fairchild, M. D. (1969). Electroencephalographic and behavioral studies of monomethylhdrazine toxicity in the cat. Technical Report AMRL-TR-69 3, Wright-Patterson Air Force Base, Ohio, Air Systems Command. A cat's ability to maintain muscular calm, explosively execute precise, complex and coordinated sequences of movements and return to a state of calm was studied by monitoring a 14 cycle brainwave. The brainwave was determined to be directly responsible for the suppression of muscular tension and spasm. It was also demonstrated that the cats could be trained to increase the strength of specific brainwave patterns associated with suppression of muscular tension and spasm. Thereafter, when the cats were administered drugs which would induce spasms, the cats which were trained to strengthen their brainwaves were resistent to the drugs.
The 12-15 Hz SMR brainwave band has been used in conventional EEG biofeedback training for rectifying pathological brain underactivation. In particular the following disorders have been treated using biofeedback training: epilepsy (as exemplified in M. B. Sterman's, M. B. 1973 work on the "Neurophysiologic and Clinical Studies of Sensorimotor EEG Biofeedback Training: Some Effects on Epilepsy" L. Birk (Ed.), Biofeedback: Behavioral Medicine, New York: Grune and Stratton); Giles de la Tourette's syndrome and muscle tics (as exemplified in the inventor's 1986 work on "A Simple and a Complex Tic (Giles de la Tourette's Syndrome): Their response to EEG Sensorimotor Rhythm Biofeedback Training", International Journal of Psychophysiology, 4, 91-97 (1986)); hyperactivity (described by M. N. Shouse, & J. F. Lubar's in the work entitled "Operant Conditioning of EEG Rhythms and Ritalin in the Treatment of Hyperkinesis", Biofeedback and Self-Regulation, 4, 299-312 (1979); reading disorders (described by M. A. Tansey, & Bruner, R. L.'s in "EMG and EEG Biofeedback Training in the Treatment of a 10-year old Hyperactive Boy with a Developmental Reading Disorder", Biofeedback and Self-Regulation, 8, 25-37 (1983)); learning disabilities related to the finding of consistent patterns for amplitudes of various brainwaves (described in Lubar, Bianchini, Calhoun, Lambert, Brody & Shabsin's work entitled "Spectral Analysis of EEG Differences Between Children with and without Learning Disabilities", Journal of Learning Disabilities, 18, 403-408 (1985)) and; learning disabilities (described by M. A. Tansey in "Brainwave signatures--An Index Reflective of the Brain's Functional Neuroanatomy: Further Findings on the Effect of EEG Sensorimotor Rhythm Biofeedback Training on the Neurologic Precursors of Learning Disabilities", International Journal of Psychophysiology, 3, 85-89 (1985)). In sum, a wide variety of disorders, whose symptomology includes impaired voluntary control of one's own muscles and a lowered cerebral threshold of overload under stress, were found to be treatable by "exercising" the supplementary and sensorimotor areas of the brain.
Conventional EEG biofeedback methods and apparatus have referenced brainwave activity in terms of large bands of EEG. As such, brainwave activity has traditionally been classified as follows: delta waves lie in the frequency range of 0 to 3.5 Hz; theta waves lie in the frequency range of 4 to 7 Hz; alpha waves lie in the frequency range of 8 to 13 Hz; beta waves lie in the frequency range above 13 HHz; and sensorimotor rhythm (SMR) waves lie in the frequency range of 12 to 15 Hz. Several patents have been directed to monitoring EEG in terms of the sensed amplitudes and percentages of alpha, theta, beta, delta and SMR waves.
U.S. Pat. No. 4,928,704 describes a biofeedback method and system for training a person to develop useful degrees of voluntary control of personal EEG activity. EEG sensors are attached to cortical sites on a head of a person for sensing EEG energy. EEG electrical energy is filtered into the pre-defined sub-bands of alpha, theta, beta and delta. Other patents directed to EEG biofeedback with alpha, theta, beta, delta and SMR waves include U.S. Pat. Nos.: 3,855,988; 4,140,997; 4,883,067; 4,919,143; 5,024,235 and European Patent No. 375,106.
U.S. Pat. No. 4,746,751 describes a system for displaying multichannel EEG data. In performing this, the procedure and method entails Evoked Response Potential signal averaging. A summed signal averaged brain map may be pieced together being comprised of reflections of the average amount of overall energy monitored over many electrode sites. In ERP, the subject receives a set of stimuli which evoke brainwaves. Other examples of patents directed to ERP include U.S. Pat. Nos.: 4,498,080; 4,926,969 and PCT Patent Application No. 8303745.
U.S. Pat. No. 4,926,969 ('969) was cited in the inventor's parent application Ser. No. 831,182. The '969 patent describes a sensory drive controller designed to detect evoked response potentials as a result of stimuli presented to the person. EEG signals taken from the subject at electrodes are amplified and filtered to enhance averaging and correlation procedures for establishing an ERP template. The ERP template is compared with detected evoked response potentials to determine the degree of similarity between the two signals.
A major disadvantage in the conventional EEG biofeedback studies has been the poor resolution of brainwave bands produced by conventional bandpass filters used as a front end for signal processing electronics. Another drawback has been that the bandpass filters are easily overloaded by an upsurge of electrical energy or high amplitude slow waves. An upsurge of electrical energy accompanies muscle movement and high amplitude slow waves activity accompanies many cerebral disorders. These unwanted, and all too frequent, signal contaminating sources are referred to as artifacts. Bandpass analysis is dependent on differential preamplifiers which multiply many thousands of times (i.e., as much as 50,000 times) the biologic signals and any accompanying artifacts. Such electronic/mechanical signal refiners contribute to inaccuracy in the monitoring of EEG signals.
Another disadvantage in conventional mechanical bandpass filtering is that arbitrary and inexact bandwidths are used to train specific brainstates. An exact analysis of waveforms is essential to EEG biofeedback protocols. Additional drawbacks to conventional systems is their reliance on multiplexors which may sample the bandpass configured signal at a rate of one sampling per second of time. For example, when monitoring a 14 cycle per second wave form 14 Hz signal, only one component will be measured, thus 13 of the 14 components of the waveform are missed. The prior art also has the disadvantage of being limited to delta, theta, alpha, beta and SMR representations.
A system for simultaneously monitoring and manipulating waveforms in a continuum from 0 to 90 Hz is not found in the prior art.